jerofad/hiv-1_progression-prediction

A kaggle competition to predict the likelihood that an HIV patient's infection will become less severe, given a small dataset and limited clinical information.

HIV-1_Progression-Prediction

A kaggle competition to predict the likelihood that an HIV patient's infection will become less severe, given a small dataset and limited clinical information.

Getting started

To be able to repeat the same experiments, you are going to need a laptop with Miniconda (a minimal version of Anaconda) and several Python packages installed which are listed in the requirements.txt file. Following instruction would work as is for Mac or Ubuntu linux users, Windows users would need to install and work in the Gitbash terminal.

Download and install Miniconda

Please go to the Anaconda website. Download and install the latest Miniconda version for Python 3.6 for your operating system.

wget <http:// link to miniconda>
sh <miniconda .sh>

After that, type:

conda --help

and read the manual.

Create a clone of this repository

You can either create a clone or make a download of this repository. To make a clone, simply use the command below on your terminal:

git clone https://github.com/jerofad/HIV-1_Progression-Prediction.git

If you are not using git, just download the repository as zip, and unpack it:

wget https://github.com/jerofad/HIV-1_Progression-Prediction/archive/master.zip
For Mac users:
curl -O https://github.com/jerofad/HIV-1_Progression-Prediction/archive/master.zip
unzip master.zip

Change into the course folder, then type:

cd HIV-1_Progression-Prediction

# To perform Data Analysis

Run the command below:

python3 data_analysis.py

The results is saved into the directory named Figures.

# To Repeat the experiment on all 10 Models

Run the command below:

python3 models.py

It saves the result into a text file named "all_models_report.txt"

# Tuning the Hyper-Parameters

If you want to train the top 5 models with parameters tuning, Run the command below

python3 parameter_tuning.py

the result of the models can be seen in the text file "models_report.txt"

# Final top 5 Models

Run the command below:

python3 train.py

the result of the models can be seen in the text file "models_report.txt"